Gender: Male 
Age: 25-34
Nationality: United Kingdom
Ethnicity: Black or African American 
Marital status: Married 
First language: English 
Religion: Christian 
Education: Doctorate or above
Mother's education: Graduate degree
Father's education: Graduate degree
Self-assessed socioeconomic status (1-10): 5
Home: Own 
Employment: Employed full time
Occupation: Technicians and associate professionals (those who apply scientific or artistic concepts and operational methods, e.g. secretaries, fitness workers, lab technicians)
Mother's occupation: Manager or business owner (those who plan, direct, coordinate and evaluate the overall activities of enterprises, governments and other organizations)
Father's occupation: Technicians and associate professionals (those who apply scientific or artistic concepts and operational methods, e.g. secretaries, fitness workers, lab technicians)
Hobbies: Exercise;Use social media;Watch sports;Go to gigs;Listen to jazz 
Devices used daily: Laptop;Smartphone;Tablet;Smartwatch 
NLP tools known: Spell checker (i.e., correcting misspellings);Grammar checker (i.e., correcting grammar mistakes);E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools used: Grammar checker (i.e., correcting grammar mistakes);Spell checker (i.e., correcting misspellings);Paraphrasing and Summarisation (i.e., automatically produce alternative descriptions of written text);Speech-to-Text (i.e. a computer transcribing your spoken language to written language);Text-to-Speech (i.e, computers being able to read out loud written language);Reading text from scanned documents (i.e. “Optical character recognition” or OCR. Given an image representing printed text, determine the corresponding text.) 
NLP tools they would find useful: E-mail spam detection (i.e., automatically send spam e-mails to your Trash folder);Sentiment analysis (e.g. automatically detect if user reviews are positive or negative)Machine translation (e.g. translate Chinese or Russian into English, and vice versa) 
Frequency of LLM use: Every day
LLMs used: ChatGPT;GitHub Copilot;Claude;HuggingChat;Microsoft Bing AI 
LLM use cases: Translation;Coding;Brainstorming;Paraphrasing or finding synonyms;Writing (e.g., composing emails);Proofreading/Editing (e.g., grammar, spelling, structure improvements);Digital content creation (e.g., creation of social media content);Solving math or logical problems;Analyze data;Answering questions about general knowledge;Get information about current events;Learning (e.g., explaining complex concepts in simple terms);Completing assignments 
Contexts of LLM use: Work;Learning;Personal;Technical;School/University 
Prompt 1: i want you to act as an seo engineering and search for word relating to finance tha people are interested in
Prompt 2: give me a list of idea for a special birthday surprise
Prompt 3: analyse the data i am about to give below and based on your analysis, provide informed decision on what to do with my enterprise
Prompt 4: help me out  with a question im finding difficult to solve
Prompt 5: writye me a 500 word email inviting staff to an official meeting
Prompt 6: rewrite this message making it unique and original while still passing same information
Prompt 7: make me a list for an kid outing including neccesary things my kid would love
Prompt 8: access this information below and give me your thought on it
Prompt 9: rewrite this letter for me making it more detailed and longer
Prompt 10: write a back end code for a simple calculator
Comments: no i don't